Grounding

Tying a model's output to verifiable external sources rather than its parametric memory.

term: Grounding
category: knowledge-memory
short_def: Tying a model's output to verifiable external sources rather than its parametric memory.
long_def: A grounded answer can cite where each claim came from. Structured data and retrievable content make grounding easier; AI answer engines increasingly cross-check claims against the live page.
see_also: rag json-ld geo
etymology_origin: — verify-against-primary-at-build ↗ https://en.wikipedia.org/wiki/Symbol_grounding_problem — 'grounding' derives from the symbol-grounding problem (Harnad, 1990); the LLM 'grounding-to-sources' sense has no single coiner
related_to: rag json-ld geo
contrast_with: Unlike RAG, which is the retrieval mechanism, grounding is the property of the output — an answer is grounded when each claim is tied to a verifiable source, however it was retrieved.
example: AI answer engines such as Perplexity ground responses by citing the live pages they pulled from, letting a reader trace each claim to its source.
source: https://en.wikipedia.org/wiki/Symbol_grounding_problem
status: active
why_it_matters: Grounding is why source-rich, structured, accurate content gets cited; a site that is easy to ground is a site that AI engines quote.
sameAs: https://en.wikipedia.org/wiki/Symbol_grounding_problem
bridge_entity: geo
last_verified: 2026-06-15
md_twin: /glossary/grounding.md